1. Data Sources Data Warehouse Analysis Results Data visualisation Analytical tools OLAP Data Mining Overview of Business Intelligence Data visualisation

2. .... From Turban, Aronson and Liang

3. Some Questions Where does the data come from? How can we decide what data is important? How can data from different sources be joined together (consolidated and integrated) securely? How can data be analysed? How can these analyses be viewed?

4. On Line Analytical Processing OLAP is a category of software technology that enables analysts, managers and executives to gain insight into data through fast, consistent, interactive access to a wide variety of possible views of information that has been transformed from raw data to reflect the real dimensionality of the enterprise as understood by the user. Fast Analysis of Shared Multidimensional Information. (FASMI)

5. Why use OLAP ? Goal: To capture the structure of real world data and provide support to the decision maker. OLAP involves many data items (many thousands or even millions) which are involved in complex relationships Fast response (<20 seconds) is crucial in OLAP OLAP functionality is characterized by dynamic multidimensional analysis of consolidated enterprise data supporting end user analytical and navigational activities.

6. What is Multidimensionality? Sometimes decision makers want to work with data in 3 or more dimensions e.g.:- A manager might want to know the sales of a given product in a geographic area, by a specific salesperson, for a particular time period. Multidimensional organisation of data allows a user to easily and quickly change the structure of tables so they will be more meaningful. Different presentations of the same data may be arranged quickly and easily

7. What do we mean by different dimensions? What do we mean by different measures?

8. Examples :Factors in Multidimensionality Dimensions –products, locations, salespeople, distribution channels, products, industries. Measures – money, sales volume, head count, actual vs forecasted Time – daily, weekly, monthly, quarterly.

9. What does Using OLAP involve? Generating queries Requesting ad hoc reports Conducting statistical analysis Building DSS and multimedia applications OLAP helps the user synthesize enterprise information through comparative, personalized viewing, as well as through analysis of historical and projected data in various "what-if" data model scenarios.

10. Analytical and Navigational Activities Calculations and modelling applied across dimensions, through hierarchies and/or across members trend analysis over sequential time periods Consolidation/Drill down Reachthrough to underlying detail data Slicing and Dicing Pivoting/Rotation to new dimensional comparisons in the viewing area

11. OLAP: examples of Navigational operations Consolidation - involves the aggregation of data i.e. simple roll-ups or complex expressions involving inter-related data e.g. sales offices can be rolled-up to districts and districts rolled-up to regions. Drill-Down can go in the reverse direction i.e. automatically display detail data which comprises consolidated data e.g.You drill-down, for example, from annual to quarterly sales figures or drill-up from shops to regions.

12. Slicing and Dicing - ability to look at the data base from different viewpoints e.g. – a slice could show sales figures for January or sales where regions where sales were below £100,000 –one slice of the sales database might show all sales of product type within regions; –another slice might show all sales by sales channel within each product type –often performed along a time axis in order to analyse trends and find patterns.

13. Pivoting allows dimensions to be viewed from any perspective. e.g. pivot a 3D cube to view different aspects of data: Sales for Q1 by Product and RegionSales for North by Product and Quarter

14. OLAP tools

15. How does OLAP work? OLAP is implemented in a multi-user client/server mode and offers consistently rapid response to queries, regardless of database size and complexity. OLAP functionality is achieved through the use of an OLAP Server.

16. OLAP SERVER - a high-capacity, multi-user data manipulation engine specifically designed to support and operate on multidimensional data structures. The design of the server and the structure of the data are optimized for rapid ad-hoc information retrieval in any orientation, as well as for fast, flexible calculation and transformation of raw data based on formulaic relationships.

17. Example – Microsoft OLAP How can data be analysed? Microsoft Online Analytical Processing (OLAP) makes it quick and easy to perform ad-hoc queries and analysis of large amounts of complex data across all aspects of your business.

18. Example – Microsoft OLAP Microsoft OLAP is used to report on... sales marketing management issues business process management budgeting and forecasting, financial issues etc..